def __init__(self, bn=False):
super(MCNN, self).__init__()
self.branch1 = nn.Sequential(Conv2d( 1, 16, 9, same_padding=True, bn=bn),
nn.MaxPool2d(2),
Conv2d(16, 32, 7, same_padding=True, bn=bn),
nn.MaxPool2d(2),
Conv2d(32, 16, 7, same_padding=True, bn=bn),
Conv2d(16, 8, 7, same_padding=True, bn=bn))
self.branch2 = nn.Sequential(Conv2d( 1, 20, 7, same_padding=True, bn=bn),
nn.MaxPool2d(2),
Conv2d(20, 40, 5, same_padding=True, bn=bn),
nn.MaxPool2d(2),
Conv2d(40, 20, 5, same_padding=True, bn=bn),
Conv2d(20, 10, 5, same_padding=True, bn=bn))
self.branch3 = nn.Sequential(Conv2d( 1, 24, 5, same_padding=True, bn=bn),
nn.MaxPool2d(2),
Conv2d(24, 48, 3, same_padding=True, bn=bn),
nn.MaxPool2d(2),
Conv2d(48, 24, 3, same_padding=True, bn=bn),
Conv2d(24, 12, 3, same_padding=True, bn=bn))
self.fuse = nn.Sequential(Conv2d( 30, 1, 1, same_padding=True, bn=bn))
python类Conv2d()的实例源码
def __init__(self, bn=False):
super(VGG16, self).__init__()
self.conv1 = nn.Sequential(Conv2d(3, 64, 3, same_padding=True, bn=bn),
Conv2d(64, 64, 3, same_padding=True, bn=bn),
nn.MaxPool2d(2))
self.conv2 = nn.Sequential(Conv2d(64, 128, 3, same_padding=True, bn=bn),
Conv2d(128, 128, 3, same_padding=True, bn=bn),
nn.MaxPool2d(2))
network.set_trainable(self.conv1, requires_grad=False)
network.set_trainable(self.conv2, requires_grad=False)
self.conv3 = nn.Sequential(Conv2d(128, 256, 3, same_padding=True, bn=bn),
Conv2d(256, 256, 3, same_padding=True, bn=bn),
Conv2d(256, 256, 3, same_padding=True, bn=bn),
nn.MaxPool2d(2))
self.conv4 = nn.Sequential(Conv2d(256, 512, 3, same_padding=True, bn=bn),
Conv2d(512, 512, 3, same_padding=True, bn=bn),
Conv2d(512, 512, 3, same_padding=True, bn=bn),
nn.MaxPool2d(2))
self.conv5 = nn.Sequential(Conv2d(512, 512, 3, same_padding=True, bn=bn),
Conv2d(512, 512, 3, same_padding=True, bn=bn),
Conv2d(512, 512, 3, same_padding=True, bn=bn))
def __init__(self, bn=False):
super(VGG16, self).__init__()
self.conv1 = nn.Sequential(Conv2d(3, 64, 3, same_padding=True, bn=bn),
Conv2d(64, 64, 3, same_padding=True, bn=bn),
nn.MaxPool2d(2))
self.conv2 = nn.Sequential(Conv2d(64, 128, 3, same_padding=True, bn=bn),
Conv2d(128, 128, 3, same_padding=True, bn=bn),
nn.MaxPool2d(2))
network.set_trainable(self.conv1, requires_grad=False)
network.set_trainable(self.conv2, requires_grad=False)
self.conv3 = nn.Sequential(Conv2d(128, 256, 3, same_padding=True, bn=bn),
Conv2d(256, 256, 3, same_padding=True, bn=bn),
Conv2d(256, 256, 3, same_padding=True, bn=bn),
nn.MaxPool2d(2))
self.conv4 = nn.Sequential(Conv2d(256, 512, 3, same_padding=True, bn=bn),
Conv2d(512, 512, 3, same_padding=True, bn=bn),
Conv2d(512, 512, 3, same_padding=True, bn=bn),
nn.MaxPool2d(2))
self.conv5 = nn.Sequential(Conv2d(512, 512, 3, same_padding=True, bn=bn),
Conv2d(512, 512, 3, same_padding=True, bn=bn),
Conv2d(512, 512, 3, same_padding=True, bn=bn))
def __init__(self, bn=False):
super(VGG16, self).__init__()
self.conv1 = nn.Sequential(Conv2d(3, 64, 3, same_padding=True, bn=bn),
Conv2d(64, 64, 3, same_padding=True, bn=bn),
nn.MaxPool2d(2))
self.conv2 = nn.Sequential(Conv2d(64, 128, 3, same_padding=True, bn=bn),
Conv2d(128, 128, 3, same_padding=True, bn=bn),
nn.MaxPool2d(2))
network.set_trainable(self.conv1, requires_grad=False)
network.set_trainable(self.conv2, requires_grad=False)
self.conv3 = nn.Sequential(Conv2d(128, 256, 3, same_padding=True, bn=bn),
Conv2d(256, 256, 3, same_padding=True, bn=bn),
Conv2d(256, 256, 3, same_padding=True, bn=bn),
nn.MaxPool2d(2))
self.conv4 = nn.Sequential(Conv2d(256, 512, 3, same_padding=True, bn=bn),
Conv2d(512, 512, 3, same_padding=True, bn=bn),
Conv2d(512, 512, 3, same_padding=True, bn=bn),
nn.MaxPool2d(2))
self.conv5 = nn.Sequential(Conv2d(512, 512, 3, same_padding=True, bn=bn),
Conv2d(512, 512, 3, same_padding=True, bn=bn),
Conv2d(512, 512, 3, same_padding=True, bn=bn))
def __init__(self, classes=None, debug=False):
super(RFCN, self).__init__()
if classes is not None:
self.classes = classes
self.n_classes = len(classes)
self.rpn = RPN()
#self.psroi_pool = PSRoIPool(7,7,1.0/16,7,15) This is for test
self.psroi_pool_cls = PSRoIPool(7,7, 1.0/16, 7, self.n_classes)
self.psroi_pool_loc = PSRoIPool(7,7, 1.0/16, 7, 8)
self.new_conv = Conv2d(512, 1024, 1, same_padding=False)
self.rfcn_score = Conv2d(1024,7*7*8, 1,1, bn=False)
self.rfcn_bbox = Conv2d(1024, 7*7*self.n_classes,1,1,bn=False)
self.bbox_pred = nn.AvgPool2d((7,7),stride=(7,7))
self.cls_score = nn.AvgPool2d((7,7),stride=(7,7))
# loss
self.cross_entropy = None
self.loss_box = None
# for log
self.debug = debug
def __init__(self, bn=False):
super(VGG16, self).__init__()
self.conv1 = nn.Sequential(Conv2d(3, 64, 3, same_padding=True, bn=bn),
Conv2d(64, 64, 3, same_padding=True, bn=bn),
nn.MaxPool2d(2))
self.conv2 = nn.Sequential(Conv2d(64, 128, 3, same_padding=True, bn=bn),
Conv2d(128, 128, 3, same_padding=True, bn=bn),
nn.MaxPool2d(2))
network.set_trainable(self.conv1, requires_grad=False)
network.set_trainable(self.conv2, requires_grad=False)
self.conv3 = nn.Sequential(Conv2d(128, 256, 3, same_padding=True, bn=bn),
Conv2d(256, 256, 3, same_padding=True, bn=bn),
Conv2d(256, 256, 3, same_padding=True, bn=bn),
nn.MaxPool2d(2))
self.conv4 = nn.Sequential(Conv2d(256, 512, 3, same_padding=True, bn=bn),
Conv2d(512, 512, 3, same_padding=True, bn=bn),
Conv2d(512, 512, 3, same_padding=True, bn=bn),
nn.MaxPool2d(2))
self.conv5 = nn.Sequential(Conv2d(512, 512, 3, same_padding=True, bn=bn),
Conv2d(512, 512, 3, same_padding=True, bn=bn),
Conv2d(512, 512, 3, same_padding=True, bn=bn))
def __init__(self):
super(RPN, self).__init__()
self.features = VGG16(bn=False)
self.conv1 = Conv2d(512, 512, 3, same_padding=True)
self.score_conv = Conv2d(512, len(self.anchor_scales) * 3 * 2, 1, relu=False, same_padding=False)
self.bbox_conv = Conv2d(512, len(self.anchor_scales) * 3 * 4, 1, relu=False, same_padding=False)
# loss
self.cross_entropy = None
self.los_box = None
def __init__(self, bn=False, num_classes=10):
super(CMTL, self).__init__()
self.num_classes = num_classes
self.base_layer = nn.Sequential(Conv2d( 1, 16, 9, same_padding=True, NL='prelu', bn=bn),
Conv2d(16, 32, 7, same_padding=True, NL='prelu', bn=bn))
self.hl_prior_1 = nn.Sequential(Conv2d( 32, 16, 9, same_padding=True, NL='prelu', bn=bn),
nn.MaxPool2d(2),
Conv2d(16, 32, 7, same_padding=True, NL='prelu', bn=bn),
nn.MaxPool2d(2),
Conv2d(32, 16, 7, same_padding=True, NL='prelu', bn=bn),
Conv2d(16, 8, 7, same_padding=True, NL='prelu', bn=bn))
self.hl_prior_2 = nn.Sequential(nn.AdaptiveMaxPool2d((32,32)),
Conv2d( 8, 4, 1, same_padding=True, NL='prelu', bn=bn))
self.hl_prior_fc1 = FC(4*1024,512, NL='prelu')
self.hl_prior_fc2 = FC(512,256, NL='prelu')
self.hl_prior_fc3 = FC(256, self.num_classes, NL='prelu')
self.de_stage_1 = nn.Sequential(Conv2d( 32, 20, 7, same_padding=True, NL='prelu', bn=bn),
nn.MaxPool2d(2),
Conv2d(20, 40, 5, same_padding=True, NL='prelu', bn=bn),
nn.MaxPool2d(2),
Conv2d(40, 20, 5, same_padding=True, NL='prelu', bn=bn),
Conv2d(20, 10, 5, same_padding=True, NL='prelu', bn=bn))
self.de_stage_2 = nn.Sequential(Conv2d( 18, 24, 3, same_padding=True, NL='prelu', bn=bn),
Conv2d( 24, 32, 3, same_padding=True, NL='prelu', bn=bn),
nn.ConvTranspose2d(32,16,4,stride=2,padding=1,output_padding=0,bias=True),
nn.PReLU(),
nn.ConvTranspose2d(16,8,4,stride=2,padding=1,output_padding=0,bias=True),
nn.PReLU(),
Conv2d(8, 1, 1, same_padding=True, NL='relu', bn=bn))
def __init__(self):
super(RPN, self).__init__()
self.features = VGG16(bn=False)
#self.features = ResNet()
self.conv1 = Conv2d(1024, 512, 3, same_padding=True)
self.score_conv = Conv2d(512, len(self.anchor_scales) * 3 * 2, 1, relu=False, same_padding=False)
self.bbox_conv = Conv2d(512, len(self.anchor_scales) * 3 * 4, 1, relu=False, same_padding=False)
# loss
self.cross_entropy = None
self.los_box = None
def __init__(self):
super(RPN, self).__init__()
self.features = VGG16(bn=False)
#self.features = ResNet()
self.conv1 = Conv2d(512, 512, 3, same_padding=True)
self.score_conv = Conv2d(512, len(self.anchor_scales) * 3 * 2, 1, relu=False, same_padding=False)
self.bbox_conv = Conv2d(512, len(self.anchor_scales) * 3 * 4, 1, relu=False, same_padding=False)
# loss
self.cross_entropy = None
self.los_box = None
def __init__(self, features):
super(RPN, self).__init__()
self.features = features
# self.features = VGG16(bn=False) # (7, 7, 512)
self.conv1 = Conv2d(512, 512, 3, same_padding=True)
self.score_conv = Conv2d(512, len(self.anchor_scales) * 3 * 2, 1, relu=False, same_padding=False)
self.bbox_conv = Conv2d(512, len(self.anchor_scales) * 3 * 4, 1, relu=False, same_padding=False)
# loss
self.cross_entropy = None
self.los_box = None
def __init__(self, use_kmeans_anchors=False):
super(RPN, self).__init__()
if use_kmeans_anchors:
print 'using k-means anchors'
self.anchor_scales = self.anchor_scales_kmeans
self.anchor_ratios = self.anchor_ratios_kmeans
self.anchor_scales_region = self.anchor_scales_kmeans_region
self.anchor_ratios_region = self.anchor_ratios_kmeans_region
else:
print 'using normal anchors'
self.anchor_scales, self.anchor_ratios = \
np.meshgrid(self.anchor_scales_normal, self.anchor_ratios_normal, indexing='ij')
self.anchor_scales = self.anchor_scales.reshape(-1)
self.anchor_ratios = self.anchor_ratios.reshape(-1)
self.anchor_scales_region, self.anchor_ratios_region = \
np.meshgrid(self.anchor_scales_normal_region, self.anchor_ratios_normal_region, indexing='ij')
self.anchor_scales_region = self.anchor_scales_region.reshape(-1)
self.anchor_ratios_region = self.anchor_ratios_region.reshape(-1)
self.anchor_num = len(self.anchor_scales)
self.anchor_num_region = len(self.anchor_scales_region)
# self.features = VGG16(bn=False)
self.features = models.vgg16(pretrained=True).features
self.features.__delattr__('30') # to delete the max pooling
# by default, fix the first four layers
network.set_trainable_param(list(self.features.parameters())[:8], requires_grad=False)
# self.features = models.vgg16().features
self.conv1 = Conv2d(512, 512, 3, same_padding=True)
self.score_conv = Conv2d(512, self.anchor_num * 2, 1, relu=False, same_padding=False)
self.bbox_conv = Conv2d(512, self.anchor_num * 4, 1, relu=False, same_padding=False)
self.conv1_region = Conv2d(512, 512, 3, same_padding=True)
self.score_conv_region = Conv2d(512, self.anchor_num_region * 2, 1, relu=False, same_padding=False)
self.bbox_conv_region = Conv2d(512, self.anchor_num_region * 4, 1, relu=False, same_padding=False)
# loss
self.cross_entropy = None
self.loss_box = None
self.cross_entropy_region = None
self.loss_box_region = None
# initialize the parameters
self.initialize_parameters()
